Магистратура
2023/2024
Анализ данных и машинное обучение
Статус:
Курс обязательный
Направление:
01.04.02. Прикладная математика и информатика
Когда читается:
1-й курс, 2 модуль
Формат изучения:
с онлайн-курсом
Онлайн-часы:
90
Охват аудитории:
для своего кампуса
Преподаватели:
Осипова Анастасия Ивановна
Прогр. обучения:
Магистр по компьютерному зрению
Язык:
английский
Кредиты:
6
Контактные часы:
12
Course Syllabus
Abstract
The course is devoted to the presentation of modern data analysis and machine learning methods that are widely used in computer vision. The main emphasis is placed on such sections as learning and inference in vision. A taxonomy of models that relate the measured image data and the actual scene content is studied. Generative and discriminative models Classification, regression and clustering methods.
Learning Objectives
- Mastering theoretical background of machine learning.
- Obtaining skills of the correct selection of methods for solving the problem
Expected Learning Outcomes
- Distinguish the main tasks of machine learning
- Process tabular data
- • Train and validate linear models for classification and regression problems
- • Apply regularization to address overfitting
- • Train and validate decision tree models for classification and regression problems
- • Distinguish components of learning error
- • Train and validate KNN model
- • Apply dimensionality reduction to data
- • Perform cluster analysis
- • Train MLP for supervised learning taks
Course Contents
- Introduction to Machine Learning
- Linear Models
- Decision Trees
- Ensemble Learning
- Unsupervised Learning
- Introduction to Artificial Neural Networks
- Final Task with instructor’s evaluation
Bibliography
Recommended Core Bibliography
- Christopher M. Bishop. (n.d.). Australian National University Pattern Recognition and Machine Learning. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsbas&AN=edsbas.EBA0C705
- Computer vision : models, learning, and inference, Prince, S. J. D., 2012
- Introduction to machine learning, Alpaydin, E., 2020
- Machine learning : a probabilistic perspective, Murphy, K. P., 2012
- McKinney, W. (2018). Python for Data Analysis : Data Wrangling with Pandas, NumPy, and IPython (Vol. Second edition). Sebastopol, CA: O’Reilly Media. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1605925
- Müller, A. C., & Guido, S. (2017). Introduction to Machine Learning with Python : A Guide for Data Scientists: Vol. First edition. Reilly - O’Reilly Media.
- Muller, A. C., & Guido, S. (2017). Introduction to machine learning with Python: a guide for data scientists. O’Reilly Media. (HSE access: http://ebookcentral.proquest.com/lib/hselibrary-ebooks/detail.action?docID=4698164)
Recommended Additional Bibliography
- Core data analysis : summarization, correlation, and visualization, Mirkin, B., 2019